English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we propose a general framework to integrate supervised andunsupervised examples with background knowledge expressed by a collection offirst-order logic clauses into kernel machines. In particular, we consider amulti-task learning scheme where multiple predicates defined on a set ofobjects are to be jointly learned from examples, enforcing a set of FOLconstraints on the admissible configurations of their values. The predicatesare defined on the feature spaces, in which the input objects are represented,and can be either known a priori or approximated by an appropriate kernel-basedlearner. A general approach is presented to convert the FOL clauses into acontinuous implementation that can deal with the outputs computed by thekernel-based predicates. The learning problem is formulated as asemi-supervised task that requires the optimization in the primal of a lossfunction that combines a fitting loss measure on the supervised examples, aregularization term, and a penalty term that enforces the constraints on boththe supervised and unsupervised examples. Unfortunately, the penalty term isnot convex and it can hinder the optimization process. However, it is possibleto avoid poor solutions by using a two stage learning schema, in which thesupervised examples are learned first and then the constraints are enforced.

Multitask Kernel-based Learning with First-Order Logic Constraints